Keras Deep Learning Cookbook

Model fit and prediction

1. Next, we will fit the model iteratively, as follows: 

for iteration in range(1, 200):    print()    print('-' * 50)    print('Iteration', iteration)    model.fit(x_train, y_train,        batch_size=BATCH_SIZE,        epochs=1,        validation_data=(x_val, y_val))    for i in range(10):        ind = np.random.randint(0, len(x_val))        rowx, rowy = x_val[np.array([ind])], y_val[np.array([ind])]        preds = model.predict_classes(rowx, verbose=0)        q = ctable.decode(rowx[0])        correct = ctable.decode(rowy[0])        guess = ctable.decode(preds[0], calc_argmax=False)        print('Q', q[::-1] if REVERSE else q, end=' ')        print('T', correct, end=' ')        if correct == guess:            print(colors.ok + '' + colors.close, end=' ')        else:            print(colors.fail + '☒' + colors.close, end=' ') print(guess) ...